In this session you will be introduced to:

1. The purpose of data visualization
2. A framework of elements of data visualization
3. BAsic types of visualization
4. How to choose the roght visualization depending on:
   • Variable type
   • Amount of variables
   • Types of properties/relationships to be highlighted

• Explore properties of the data
• Reveal insights to be found in the data
• Create data-narratives
• …

Q: What is wrong with this data visualization?

Borner, K. (2015). Atlas of knowledge: Anyone can map MIT Press.

Q1: What insight do I want to gain/communicate with this visualization?

• Distribution?
• Composition?
• Cluster?
• Trends (over time)?
• Position in space?
• Correlation, relationships?
• Statistical properties?

Different scales….

• … allow to ask different questions
• … require different means of presentation

Often, we do not only look at raw data, but aim at visualizing result of an analysis. Again, different analysis offer/require different forms of visualization.

• When: Temporal Analysis / Timeseries
• Where: Geospatial Analysis
• What: Topical Data Analysis
• Why: Inferential Statistics
• With whom: Network Analysis

• Often, the position in 2-dimensional space represent the first ways to map information in th data.
• We refer to this 2d mapping as the choice of reference system.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

The shape of elements plotted in the reference system represent another dimension to communicate (discrete) data properties. Eg.

• Points
• Lines
• Linguistic symbols

Graphical variables allow further dimensions to communicate (discrete or contineous) data properties

In combinations, shapes and variable mappings allow for multiple types of information expressed jointly.

Interactive visualizations allow for

• communicating more dynamic and complex properties/relationships
• Allow own insight-creation by exploration

Scatterplot (2 variables)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point

Scatterplot (3 variables, 2c1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Species)

Scatterplot (3 variables, 3c)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Petal.Lenght)

Scatterplot (4 variables, 3c,1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Petal.Lenght), Shape (Species)

Facet Matrix (4 variables, 2c,1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Y-Facet: Species

Boxplot (Univariate distribution of multiple variables)

Reference system:

• y = Value Variable
• x = Value variable x
• Symbol = Confidence Interval Box

Correlation Matrix (bivariate distribution of multiple variables)

Reference system:

• y = Variable
• x = Variable
• VAriable: Color (Correlation)

Correlation Matrix (bivariate distribution of multiple variables)

## Warning in warn_if_args_exist(list(...)): Extra arguments: 'ggtheme' are being
## ignored. If these are meant to be aesthetics, submit them using the 'mapping'
## variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

Reference system:

• y = Value Variable
• x = Value variable x
• Symbol = Confidence Intervall Box

Examples ar manifold. Just to give you one:

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

## Warning: All elements of `...` must be named.
## Did you want `key = c(key)`?

## Warning: All elements of `...` must be named.
## Did you want `key = c(key)`?

## Warning: Can only have one: highlight

## Warning: All elements of `...` must be named.
## Did you want `key = c(key)`?

## Warning: Can only have one: highlight

## Warning: All elements of `...` must be named.
## Did you want `key = c(key)`?

## Warning: Can only have one: highlight

## Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

• Data visualization is of high importance for data exploration, insight generation & communication
• Depending on the purpose of th visualization, different types have to be chosen.
• Variable characteristics influence the possibilities of visual mapping.
• Depending on type & amount of relationships to be depicted, different visualization devices can b utulized.
• Common mapping elements are: Reference position (x, y), color, shape, alpha, facet